The Own-Race Recognition Advantage is Attributable to Visual Working Memory: Evidence from a continuous-response paradigm
Bibliographic record
Abstract
Considerable research examining the other-race effect (e.g., better recognition of own-race than other-race faces) has proposed that impaired recognition of other-race faces can be attributed to the inefficient storage and retrieval of other-race face representations from memory. However, little is known about the precision with which own- versus other-race faces are mentally represented in visual working memory (VWM). To address the question, we used a continuous-response paradigm and a mixture model to independently measure the precision (sd) and number of own-and other-race face representations stored in VWM. We created a set of Caucasian and Asian face stimuli by morphing between all possible pairings of four Caucasian and four Asian identities. In the experiment, two morphed faces, cued by different colors, were presented for 1500 ms and followed by a 900 ms delay. Participants then were instructed to recall one of the two faces (i.e., target face cued by a specific color) from memory by clicking the target face from a "face wheel", comprising four anchor faces and a morphed continuum between adjacent pairs (e.g., A-B; B-C; C-D; D-A). Based on the mixture-model analysis, the number of other-race face representations correctly reported (M = 57.4%) was reduced compared to that of own-race faces (M = 77.9%). However, the precision of those representations was comparable for own- and other-race faces (Msd = 35.40 and 33.23, respectively), as was the probability of incorrectly selecting the non-target face (discrimination error) (Me = 0.19% vs. 0.17%, for own- and other-race faces, respectively). The current study provides direct evidence of a fundamental difference in how own- and other-race faces are represented in visual working memory and highlights the functional role of perceptual experience in shaping such representations. Meeting abstract presented at VSS 2016
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".